Engineered materials are ubiquitous throughout society and are critical to the development
of modern technology, yet many current material systems are inexorably tied to widespread …

Since the discovery of deep eutectic solvents (DESs) in 2003, significant progress has been
made in the field, specifically advancing aspects of their preparation and physicochemical …

We report a flexible multi-modal mechanics language model, MeLM, applied to solve
various nonlinear forward and inverse problems, that can deal with a set of instructions …

Machine learning (ML) has emerged as an indispensable methodology to describe,
discover, and predict complex physical phenomena that efficiently help us learn underlying …

Inspired by natural materials, hierarchical architected materials can achieve enhanced
properties including achieving tailored mechanical responses. However, the design space …

We report a series of deep learning models to solve complex forward and inverse design
problems in molecular modeling and design. Using both diffusion models inspired by …

Architected materials can achieve enhanced properties compared to their plain
counterparts. Specific architecting serves as a powerful design lever to achieve targeted …

Large language models (LLMs), such as ChatGPT and PaLM, are able to perform
sophisticated text comprehension and generation tasks with little or no training. Alongside …

The prediction of mechanical and dynamical properties of proteins is an important frontier,
especially given the greater availability of proteins structures. Here we report a series of …

In this study we report a computational approach towards multiscale architected materials
analysis and design. A particular challenge in modeling and simulation of materials, and …